Quantum Spider Monkey Optimization (QSMO) Algorithm for Automatic Gray-Scale Image Clustering

In automatic image clustering, high homogeneity of each cluster is always desired. The increase in number of thresholds in gray scale image segmentation/clustering poses various challenges. Recent times have witnessed the growing popularity of swarm intelligence based algorithms in the field of image segmentation. The Spider Monkey Optimization (SMO) algorithm is a notable example, which is motivated by the intelligent behavior of the spider monkeys. The SMO is broadly categorized as a fission-fusion social structure based intelligent algorithm. The original version of the algorithm as well as its variants have been successfully used in several optimization problems. The current work proposes a quantum version of SMO algorithm which takes recourse to quantum encoding of its population along with quantum variants of the intrinsic operations. The basic concepts and principles of quantum mechanics allows QMSO to explore the power of computing. In QMSO, qubits designated chromosomes operate to drive the solution toward better convergence incorporating rotation gate in Hilbert hyperspace. A fitness function associated with maximum distance between cluster centers have been introduced. An application of the proposed QSMO algorithm is demonstrated on the determination of automatic clusters from real life images. A comparative study with the performance of the classical SMO shows the efficacy of the proposed QSMO algorithm.